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MBPP+ takes the 378 problems of the sanitized MBPP set and multiplies their tests by roughly 35 times. It removes the same test-weakness inflation that HumanEval+ addresses.
mbpp-plus
Full text: eligible public problems include text, images and discussion.
The EvalPlus project developed MBPP+ to strengthen the execution-based evaluation of programs on MBPP. Its Hugging Face distribution was published in January 2024. The motivation was to catch incorrect programs that pass a small original test suite, a problem also addressed by HumanEval+.
MBPP+ retains the 378 programming problems of the sanitized MBPP set and expands their tests by roughly 35 times. The task remains a short natural-language programming request, with candidate programs evaluated by running the expanded assertions. This catalogue imports 200 of the 378 problems and keeps reference programs and expanded assertions outside the displayed prompt.
A short natural-language programming task. The reference program and the expanded assertions are not part of the prompt.
Correctness is decided by executing the candidate against the expanded assertions, so the reference program the source ships is not a graded answer; it stays in restricted storage.
Metric: pass@1 on the expanded tests
Each task is a few lines of Python, but the specification is one sentence long, so most of the difficulty is in reading what is actually being asked for.
Additional tests provide stronger evidence of correctness but cannot prove that a program satisfies a specification for every possible input. Short task descriptions can also leave edge cases underspecified, making the reference implementation and tests consequential parts of the evaluation. The benchmark covers small programming tasks rather than repository-scale development, and expanding tests does not remove potential exposure to the original public problems.
@article{liu2023mbppplus,
title={Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation},
author={Jiawei Liu and Chunqiu Steven Xia and Yuyao Wang and Lingming Zhang},
journal={arXiv preprint arXiv:2305.01210},
year={2023}
}1 thread and 0 posts on this page. Anyone may reply; no account is required.
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Whole public benchmark.
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b2d74c91837c3f2a20c1299ae98133cbe7cfa077 · acquired 2026-09-12 · release 10a7a2abecadb2d74c91837c3f2a20c1299ae98133cbe7cfa077 · acquired 2026-09-12 · release 532e203491c3b2d74c91837c3f2a20c1299ae98133cbe7cfa077 · acquired 2026-09-12 · release 147b31500dd9Answer published by the source
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